60–95% Fewer Tokens with Claude Code and Codex

If you ever open the billing dashboard and you feel something cold, this article is exactly written for you.

I can’t remember how many times a single conversation has burned through six figures of tokens in my projects.

Easiest way to achieve the unnecessary goal is to hit a search API that returns hundred JSON objects and pastes it in a stack trace.

None of it probably felt expensive in isolation but added up, on a model where output costs five times input and every turn re-sends the entire transcript, it is a tax I was paying on every single call for the rest of the session.

So that why I want to explain how context compressors work in this article.

Every byte a compressor removes is a byte your model no longer reasons over but you have to remember that the only metric that matters here is task-completion accuracy under the lossy view.

To explain the concepts and usage, I’ll use open-source tool Headroom, built by Netflix engineer Tejas Chopra.

It’s a thoughtfully designed piece of software, and reading its own numbers carefully is a way to…